L’objectif de cette note est de guider les chercheuses et chercheurs effectuant une mission de terrain dans le Makay début octobre. Il s’agit de nourrir l’articulation entre les bras socioéconomique, ethnoécologique et anthropologique de l’étude en documentant à partir de sources tierces (données satellitaires principalement) des évènements concrets susceptibles d’être appréhendées dans chacun des bras. On a ainsi identifié des passages de cyclones, feux de brousse/forêt et périodes de pluie/sécheresse. De par leur nature, ces évènements concrets sont circonscrits dans le temps et l’espace et documenter leur chronologie et leur localisation dans les enquêtes de chacun des bras favorisera l’étalonnage, la comparabilité et les échanges entre les bras. Concrètement, un feu de brousse de grande ampleur survenu dans une zone déterminée est susceptible d’influencer les résultats de l’enquête OR pour les localités avoisinantes (bras socioéconomique), les écosystèmes et leur perception par les populations locales (bras ethnoécologique), ou encore les représentations captées par le bras anthropologique.
Par souci de transparence et pour être en mesure de reproduire les analyses plus tard, en actualisant ou non les données sources, on travaille sur R en mode notebook. Un seul document, archivé et versionné de manière rigoureuse, contient à la fois le texte d’analyse, les traitements de données (bloc de code ci-dessous) et les visualisations de résultats.
# Ci-dessous les librairies R utilisées pour cette analyse
library(tidyverse) # pour la manipulation et visualisation de données
library(sf) # pour traiter les données spatiales
library(tmap) # pour générer des cartes
library(aws.s3) # Pour accéder aux données volumineuses stockées en ligne
library(geodata) # Pour avoir les frontières administratives
library(curl) # Pour télécharge
library(lubridate) # Pour manipuler des dates
library(httr)Nous avons les coordonnées suivantes:
(Il s’agit d’un format “Degree Decimal Minutes (DDM)”: pas de secondes fournies, mais des décimales de minutes).
Nous les importons et les visualisons :
# Create a dataframe of the coordinates
coordinates <- data.frame(
location = c("Beroroha", "Beronono", "Tsivoko", "Makaykely", "Bivouac"),
latitude = c("21°40.504’S", "21°21.669’S", "21°17.712’S", "21°28.074’S", "21°13.369’S"),
longitude = c("45°9.571’E", "45°14.885’E", "45°22.732’E", "45°21.896’E", "45°19.516’E")
)
# Function to parse DMS and convert to decimal degrees
ddm_to_decimal <- function(dms){
# Extraire les degrés, minutes et secondes
parts <- regmatches(dms, gregexpr("[0-9.]+", dms))[[1]]
degree <- as.numeric(parts[1])
minute <- as.numeric(parts[2]) / 60
direction <- ifelse(grepl("S|W", dms), -1, 1)
# Converstion au format décimal
decimal <- direction * (degree + minute)
return(decimal)
}
# Apply function to each coordinate
coordinates$latitude_decimal <- sapply(coordinates$latitude, ddm_to_decimal)
coordinates$longitude_decimal <- sapply(coordinates$longitude, ddm_to_decimal)
# Convertir les coordonnées en objet sf
coordinates_sf <- st_as_sf(coordinates,
coords = c("longitude_decimal", "latitude_decimal"),
crs = 4326)
makay_ap <- st_read("data/Makay_wgs84.geojson", quiet = TRUE) %>%
rename(Statut = SOURCETHM) %>%
mutate(Statut = recode(Statut, "zone tampon" = "Zone tampon"))
# Fusion des différentes aires
makay_union <- makay_ap %>%
st_union() %>%
st_sf() %>%
st_make_valid()
# Carte superposant les différentes informations
tmap_mode("view")
tm_shape(makay_ap) +
tm_polygons(col = "Statut", alpha = 0.5) +
tm_shape(makay_union) +
tm_borders(col = "darkblue") +
tm_shape(coordinates_sf) +
tm_dots(col = "purple", size = 0.1, labels = coordinates_sf$location) +
tm_text(text = "location", xmod = 9) +
tm_basemap("OpenStreetMap") +
tm_layout(title = "Locations", main.title.size = 1.5) +
tm_add_legend(type = "fill", col = "darkblue", labels = "Limite externe")Localisation prévue des visites de terrain
A partir de cette information, on va visualiser des informations récentes qui seraient intéressantes à documenter lors des entretiens de terrain.
Pour cette visualisation, nous avons mobilisé des données provenant du satellite VIIRS (Visible Infrared Imaging Radiometer Suite). Les données téléchargées depuis le service FIRMS de la NASA portent uniquement sur les deux derniers mois. Elles incluent plusieurs variables, parmi lesquelles nous utilisons principalement la variable ‘frp’ (Fire Radiative Power) pour représenter l’intensité des incendies. Cette variable, exprimée en mégawatts (MW), quantifie le taux d’émission radiative des feux actifs, offrant une indication directe de l’intensité du feu. De plus, nous avons défini une zone d’intérêt autour de la zone où l’enquête terrain sera réalisé et nous visualisons les données à l’intérieur de cette zone. La carte finale montre ces incendies avec une gradation de couleurs pour refléter l’intensité de chaque incendie.En cliquant sur les points, on peut consulter la date à laquelle le foyer a été enregistré par le satellite, ainsi que la probabilité qu’il s’agisse téellement d’un feu (classé en “élevée”, “nominale” et “faible”).
# Find my token
if (Sys.info()["effective_user"] == "onyxia") {
system("vault kv get -format=table onyxia-kv/fbedecarrats/earth_data",
intern = TRUE) -> my_secret
my_key_api <- my_secret %>%
pluck(17) %>%
str_remove("^\\b\\w+\\b *")
} else { # otherwise you need to store your file encryption key
# in a file named secret_zip_key
my_key_api <- readLines("secret_api_key")
}
# Ensure folders are available
dir.create("data/firms")
target_dir <- "data/firms"
# Download csv download list from NASA Earth data
csv_url <- "https://nrt4.modaps.eosdis.nasa.gov/api/v2/content/details/FIRMS/noaa-20-viirs-c2/Southern_Africa?fields=all&formats=csv"
csv_file <- "firms_data.csv"
download.file(csv_url, csv_file)
# Prepare download
firms_data <- read_csv(csv_file)
# A function to check locally and download missing files
download_earthdata <- function(url, outdir = "data/firms",
token = my_key_api) {
file_name <- basename(url)
target_path <- file.path(outdir, file_name)
header <- add_headers(Authorization = paste("Bearer", token))
# Check if the file is missing locally
if (!file.exists(target_path)) {
GET(url, header,
write_disk(target_path))
# print(url)
}
}
# Using purrr's map to iterate over each row of the dataframe
purrr::map(firms_data$downloadsLink, download_earthdata)[[1]]
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<ON DISK> C:\Users\fbede\Documents\Statistiques\available_data_makay\data\firms\J1_VIIRS_C2_Southern_Africa_VJ114IMGTDL_NRT_2023269.txtfirms <- list.files("data/firms", full.names = TRUE) %>%
map_df(read_csv)
firms_sf <- firms %>%
st_as_sf(coords = c("longitude", "latitude"), crs = 4326)
buffer <- coordinates_sf %>%
filter(location == "Beronono") %>%
st_buffer(100000)
firms_focus <- firms_sf %>%
st_intersection(buffer)
breaks_frp <- c(min(firms_focus$frp),
median(firms_focus$frp),
max(firms_focus$frp))
breaks_frp_5 <- quantile(firms_focus$frp, probs = seq(0, 1, by = 0.25),
na.rm = TRUE)
tm_shape(firms_focus) +
tm_dots(col = "frp", size = 0.2, alpha = 0.2,
palette = c("blue", "cyan", "yellow", "orange", "red"),
border.col = NULL, breaks = breaks_frp_5,
popup.vars = c("acq_date", "confidence", "frp"),
title = "Fire radiative power") +
tm_layout(title = "Fires around Beronono") +
tm_shape(makay_union) +
tm_borders(col = "darkblue") +
tm_shape(coordinates_sf) +
tm_dots(col = "purple", size = 0.1, labels = coordinates_sf$location) +
tm_view(set.view = c(10)) +
tm_text(text = "location", ymod = -0.5) +
tm_basemap("OpenStreetMap") +
tm_scale_bar()Cette carte pourra être rafraichie jour après jour. A la date d’aujourd’hui (26/09/2023), on constate relativement peu d’incendie. Un semble avoir eu lieu à 4km au Nord-Est de Beroroha le 09/08/2023, avec un FRP de 21, mais il ne semble pas avoir duré plus d’une journée. Une série de foyers ont aussi été relevés le 25/08/2023 à 3km à l’Est de Beronono.
On utilise les données du Tropical cyclone best track (IBTrACS) pour obtenir un historique du passage de cylones et leurs attributs [@knapp_international_2010], ainsi que du code source de [@mapme.protectedareas].
if (!file.exists("data/ibtracs/IBTrACS.since1980.list.v04r00.lines.shp")) {
dir.create("data/ibtracs")
download.file(url = "https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/shapefile/IBTrACS.since1980.list.v04r00.lines.zip",
destfile = "data/ibtracs/ibtracs_lines.zip")
unzip("data/ibtracs/ibtracs_lines.zip", exdir = "data/ibtracs")
}
# column description is given here: https://www.ncei.noaa.gov/sites/default/files/2021-07/IBTrACS_v04_column_documentation.pdf
cyclones <- read_sf("data/ibtracs/IBTrACS.since1980.list.v04r00.lines.shp")
mada <- gadm("MDG", level=0, path = "data") %>%
st_as_sf()
# Create a buffer 300km around Madagascar
mada_buff <- st_buffer(mada, 300)
cyclones$wind_combined <- cyclones %>%
select(contains("WIND")) %>%
select(-WMO_WIND) %>%
st_drop_geometry() %>%
rowMeans(., na.rm = T)
cyclones_mada <- cyclones %>%
st_intersection(mada_buff) %>%
select(`Année` = SEASON, Nom = NAME, `Vents en noeuds` = wind_combined)
tm_shape(makay_union) +
tm_borders(col = "darkblue") +
tm_shape(cyclones_mada) +
tm_lines(col = "Année", lwd = "Vents en noeuds", scale = 3, palette = "Blues",
popup.vars = c("Année", "Année", "Vents en noeuds"),
legend.format = list(big.mark=""), popup.format = list(big.mark=""))Cyclone trajectories, dates and intensity (source: IBTrACS)
La carte montre que l’aire protégée de Makay a été frappée par plusieurs cyclones importants. En particulier, Freddy en 2023 et Bastiraï en 2022, tous deux avec des vents d’environ 57 nœuds (1 nœud = 1,825 km/h). Des cyclones de moindre importance avaient déjà frappé la région les années précédentes : Chedza en 2014 (vents d’environ 42 nœuds), Elita en 2005 (vents d’environ 52 nœuds).